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"""
Train and eval functions used in main.py
"""
import math
import sys
from typing import Iterable, Optional

import torch

from timm_need.data import Mixup
from timm.utils import accuracy, ModelEma
import apex.amp
import utils
import pdb


def train_one_epoch(model: torch.nn.Module, criterion: torch.nn.Module,
                    data_loader: Iterable, optimizer: torch.optim.Optimizer,
                    device: torch.device, epoch: int, loss_scaler, max_norm: float = 0,
                    model_ema: Optional[ModelEma] = None, mixup_fn: Optional[Mixup] = None,
                    set_training_mode=True
                    ):
    # TODO fix this for finetuning
    model.train(set_training_mode)
    criterion.train()
    metric_logger = utils.MetricLogger(delimiter="  ")
    metric_logger.add_meter('lr', utils.SmoothedValue(window_size=1, fmt='{value:.6f}'))
    header = 'Epoch: [{}]'.format(epoch)
    print_freq = 10
    
    for samples, targets in metric_logger.log_every(data_loader, print_freq, header): #等价 for samples, targets in data_loader 
        
        #pdb.set_trace() 
        
        samples = samples.to(device, non_blocking=True)
        targets = targets.to(device, non_blocking=True)

        if mixup_fn is not None:
            samples, targets = mixup_fn(samples, targets)

        #with torch.cuda.amp.autocast():
        outputs = model(samples)
        if isinstance(outputs, list):
            loss_list = [criterion(o, targets) / len(outputs) for o in outputs]
            loss = sum(loss_list)
        else:
            loss = criterion(outputs, targets)

        loss_value = loss.item()

        if not math.isfinite(loss_value):
            print("Loss is {}, stopping training".format(loss_value))
            sys.exit(1)

        optimizer.zero_grad()
        
        # this attribute is added by timm on one optimizer (adahessian)
        is_second_order = hasattr(optimizer, 'is_second_order') and optimizer.is_second_order

        with apex.amp.scale_loss(loss, optimizer) as scaled_loss:
            scaled_loss.backward(create_graph=is_second_order)

        optimizer.step()
        # loss_scaler(loss, optimizer, clip_grad=max_norm,
        #            parameters=model.parameters(), create_graph=is_second_order)

        # torch.npu.synchronize()
        # if model_ema is not None:
        #    model_ema.update(model)

        '''
        # this attribute is added by timm on one optimizer (adahessian)
        is_second_order = hasattr(optimizer, 'is_second_order') and optimizer.is_second_order
        loss_scaler(loss, optimizer, clip_grad=max_norm,
                    parameters=model.parameters(), create_graph=is_second_order)

        torch.cuda.synchronize()
        if model_ema is not None:
            model_ema.update(model)
        '''
        if isinstance(outputs, list):
            metric_logger.update(loss_0=loss_list[0].item())
            metric_logger.update(loss_1=loss_list[1].item())
        else:
            metric_logger.update(loss=loss_value)
        metric_logger.update(lr=optimizer.param_groups[0]["lr"])
    # gather the stats from all processes
    metric_logger.synchronize_between_processes()
    print("Averaged stats:", metric_logger)
    return {k: meter.global_avg for k, meter in metric_logger.meters.items()}


@torch.no_grad()
def evaluate(data_loader, model, device):
    criterion = torch.nn.CrossEntropyLoss()

    metric_logger = utils.MetricLogger(delimiter="  ")
    header = 'Test:'

    # switch to evaluation mode
    model.eval()

    for images, target in metric_logger.log_every(data_loader, 10, header):
        images = images.to(device, non_blocking=True)
        target = target.to(device, non_blocking=True)

        # compute output
        #with torch.cuda.amp.autocast():
        output = model(images)
        # Conformer
        if isinstance(output, list):
            loss_list = [criterion(o, target) / len(output)  for o in output]
            loss = sum(loss_list)
        # others
        else:
            loss = criterion(output, target)

        if isinstance(output, list):
            # Conformer
            acc1_head1 = accuracy(output[0], target, topk=(1,))[0]
            acc1_head2 = accuracy(output[1], target, topk=(1,))[0]
            acc1_total = accuracy(output[0] + output[1], target, topk=(1,))[0]
        else:
            # others
            acc1, acc5 = accuracy(output, target, topk=(1, 5))

        batch_size = images.shape[0]
        if isinstance(output, list):
            metric_logger.update(loss=loss.item())
            metric_logger.update(loss_0=loss_list[0].item())
            metric_logger.update(loss_1=loss_list[1].item())
            metric_logger.meters['acc1'].update(acc1_total.item(), n=batch_size)
            metric_logger.meters['acc1_head1'].update(acc1_head1.item(), n=batch_size)
            metric_logger.meters['acc1_head2'].update(acc1_head2.item(), n=batch_size)
        else:
            metric_logger.update(loss=loss.item())
            metric_logger.meters['acc1'].update(acc1.item(), n=batch_size)
            metric_logger.meters['acc5'].update(acc5.item(), n=batch_size)
    if isinstance(output, list):
        print('* Acc@heads_top1 {heads_top1.global_avg:.3f} Acc@head_1 {head1_top1.global_avg:.3f} Acc@head_2 {head2_top1.global_avg:.3f} '
              'loss@total {losses.global_avg:.3f} loss@1 {loss_0.global_avg:.3f} loss@2 {loss_1.global_avg:.3f} '
              .format(heads_top1=metric_logger.acc1, head1_top1=metric_logger.acc1_head1, head2_top1=metric_logger.acc1_head2,
                      losses=metric_logger.loss, loss_0=metric_logger.loss_0, loss_1=metric_logger.loss_1))
    else:
        print('* Acc@1 {top1.global_avg:.3f} Acc@5 {top5.global_avg:.3f} loss {losses.global_avg:.3f}'
          .format(top1=metric_logger.acc1, top5=metric_logger.acc5, losses=metric_logger.loss))
    return {k: meter.global_avg for k, meter in metric_logger.meters.items()}
